Teaching

My teaching combines rigorous methodological training with substantive questions across the social sciences, preparing students to conduct transparent, reproducible, and policy-relevant research. I teach quantitative and qualitative research methods at both the undergraduate and graduate levels, with expertise in causal inference, Bayesian statistics, time series analysis, and computational social science. My methods courses emphasize project-based learning using real-world datasets, reproducible workflows, and modern computational tools. My substantive teaching focuses on international political economy, development, institutions, and formal theory.

At NYU, I have designed and taught undergraduate and graduate courses in data science. Beyond the classroom, I view mentoring as an integral part of teaching. I currently supervise over a dozen undergraduate researchers at NYU on collaborative research projects, building on earlier experience mentoring undergraduate research assistants at WashU. Whether students are learning statistical methods or exploring political institutions, I encourage them to connect theoretical arguments with empirical evidence and to engage actively with contemporary challenges.

Courses Taught

Teaching Interests

Data Science & Research Methods: Introduction to Data Science; Programming with Python and R; Statistical Inference; Applied Statistical Modeling; Data Visualization; Machine Learning; Causal Inference; Bayesian Data Analysis; Time Series and Panel Data; Research Design; Computational Social Science

Political Science and Public Policy: International Political Economy; Political Economy of Development; Formal Theory; Institutions and Governance; Global Politics; Foreign Aid; Politics of South Asia